A pedometric technique to delimitate soil-specific zones at field scale
Delimitation of soil types within a farm field is key for site-specific crop management. An alternative to this, is to develop pedometric techniques that allow an efficient combination of soil survey information and high-resolution terrain attribute data. The aim of this study was to present and eva...
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| Format: | Artículo |
| Language: | Inglés |
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2018
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| Online Access: | http://hdl.handle.net/20.500.12123/2130 https://www.sciencedirect.com/science/article/pii/S0016706117302884 https://doi.org/10.1016/j.geoderma.2018.02.034 |
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| author | Castro Franco, Mauricio Córdoba, Mariano Augusto Balzarini, Mónica Graciela Costa, Jose Luis |
| author_browse | Balzarini, Mónica Graciela Castro Franco, Mauricio Costa, Jose Luis Córdoba, Mariano Augusto |
| author_facet | Castro Franco, Mauricio Córdoba, Mariano Augusto Balzarini, Mónica Graciela Costa, Jose Luis |
| author_sort | Castro Franco, Mauricio |
| collection | INTA Digital |
| description | Delimitation of soil types within a farm field is key for site-specific crop management. An alternative to this, is to develop pedometric techniques that allow an efficient combination of soil survey information and high-resolution terrain attribute data. The aim of this study was to present and evaluate a pedometric technique to delimit soil-specific zones at field scale by coupled Random forest, fuzzy k-means clustering and spatial principal components algorithms (RF-KM-sPCA) and by using information from soil surveys and terrain attributes derived from a digital elevation model. The protocol involves three-steps: 1) automatic classification of small (20x20m) spatial units (SU) using the knowledge of the soil map units present in the farm landscape, 2) aggregation of SUM at farm scale and 3) validation of soil-specific zones. For the first step, we used the random forest algorithm with 10 terrain attributes. For the second step, KM-sPCA algorithms were used to cluster within field SU accounting for autocorrelation. For the third step, apparent soil electrical conductivity and yield maps was used to validate the delimitation of soil-specific zones. This technique produced more contiguous zones than other cluster methods which do not use spatiality. Six farm fields with highly differences in soils were partitioned by the proposed pedometric strategy. Apparent soil electrical conductivity and yield maps present significant differences among zones in all experimental fields. This analytic strategy, based in easy-to-obtain data, could be used to improve precision agricultural managements. |
| format | Artículo |
| id | INTA2130 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| record_format | dspace |
| spelling | INTA21302019-03-21T18:49:35Z A pedometric technique to delimitate soil-specific zones at field scale Castro Franco, Mauricio Córdoba, Mariano Augusto Balzarini, Mónica Graciela Costa, Jose Luis Suelo Agricultura de Precisión Manejo del Cultivo Reconocimiento de Suelos Soil Precision Agriculture Crop Management Soil Surveys Delimitation of soil types within a farm field is key for site-specific crop management. An alternative to this, is to develop pedometric techniques that allow an efficient combination of soil survey information and high-resolution terrain attribute data. The aim of this study was to present and evaluate a pedometric technique to delimit soil-specific zones at field scale by coupled Random forest, fuzzy k-means clustering and spatial principal components algorithms (RF-KM-sPCA) and by using information from soil surveys and terrain attributes derived from a digital elevation model. The protocol involves three-steps: 1) automatic classification of small (20x20m) spatial units (SU) using the knowledge of the soil map units present in the farm landscape, 2) aggregation of SUM at farm scale and 3) validation of soil-specific zones. For the first step, we used the random forest algorithm with 10 terrain attributes. For the second step, KM-sPCA algorithms were used to cluster within field SU accounting for autocorrelation. For the third step, apparent soil electrical conductivity and yield maps was used to validate the delimitation of soil-specific zones. This technique produced more contiguous zones than other cluster methods which do not use spatiality. Six farm fields with highly differences in soils were partitioned by the proposed pedometric strategy. Apparent soil electrical conductivity and yield maps present significant differences among zones in all experimental fields. This analytic strategy, based in easy-to-obtain data, could be used to improve precision agricultural managements. CEI Barrow Fil: Castro Franco, Mauricio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Barrow; Argentina Fil: Córdoba, Mariano Augusto. Universidad Nacional de Cordoba. Facultad de Ciencias Agropecuarias. Cátedra de Estadística y Biometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Balzarini, Mónica Graciela. Universidad Nacional de Cordoba. Facultad de Ciencias Agropecuarias. Cátedra de Estadística y Biometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Costa, Jose Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina 2018-03-27T12:28:27Z 2018-03-27T12:28:27Z 2018-07 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/2130 https://www.sciencedirect.com/science/article/pii/S0016706117302884 0016-7061 https://doi.org/10.1016/j.geoderma.2018.02.034 eng info:eu-repo/semantics/restrictedAccess application/pdf Geoderma 322 : 101-111. (July 2018) |
| spellingShingle | Suelo Agricultura de Precisión Manejo del Cultivo Reconocimiento de Suelos Soil Precision Agriculture Crop Management Soil Surveys Castro Franco, Mauricio Córdoba, Mariano Augusto Balzarini, Mónica Graciela Costa, Jose Luis A pedometric technique to delimitate soil-specific zones at field scale |
| title | A pedometric technique to delimitate soil-specific zones at field scale |
| title_full | A pedometric technique to delimitate soil-specific zones at field scale |
| title_fullStr | A pedometric technique to delimitate soil-specific zones at field scale |
| title_full_unstemmed | A pedometric technique to delimitate soil-specific zones at field scale |
| title_short | A pedometric technique to delimitate soil-specific zones at field scale |
| title_sort | pedometric technique to delimitate soil specific zones at field scale |
| topic | Suelo Agricultura de Precisión Manejo del Cultivo Reconocimiento de Suelos Soil Precision Agriculture Crop Management Soil Surveys |
| url | http://hdl.handle.net/20.500.12123/2130 https://www.sciencedirect.com/science/article/pii/S0016706117302884 https://doi.org/10.1016/j.geoderma.2018.02.034 |
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